Image annotation with discriminative model and annotation refinement by visual similarity matching

A large percentage of photos on the Internet cannot be reached by search engines because of the absence of textual metadata. Such metadata come from description and tags of the photos by their uploaders. Despite of decades of research, neither model based and model-free approaches can provide quality annotation to images. In this thesis, I present a hybrid annotation pipeline that combines both approaches in hopes of increasing the accuracy of the resulting annotations. Given an unlabeled image, the first step is to suggest some words via a trained model optimized for retrieval of images from text. Though the trained model cannot always provide highly relevant words, they can be used as initial keywords to query a large web image repository and obtain text associated with retrieved images. We then use perceptual features (e.g., color, texture, shape, and local characteristics) to match the retrieved images with the query photo and use visual similarity to rank the relevance of suggested annotations for the query photo.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Hu, Rong (RongRong)

Advisor dc:contributor.advisor

• Edward Chang and Berthold Horn.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/61311

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/61311